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<table width="100%" summary="page for decontaminants"><tr><td>decontaminants</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>
Performance of decontaminants used in the culturing of a micro-organism
</h2>

<h3>Description</h3>

<p>The two decontaminants 1-hexadecylpyridium chloride and oxalic acid were used. Additionally there was a control group (coded as concentration 0 and only included under oxalic acid).
</p>


<h3>Usage</h3>

<pre>data("decontaminants")</pre>


<h3>Format</h3>

<p>A data frame with 128 observations on the following 3 variables.
</p>

<dl>
<dt><code>conc</code></dt><dd><p>a numeric vector of percentage weight per volume</p>
</dd>
<dt><code>count</code></dt><dd><p>a numeric vector of numbers of M. bovis colonies at stationarity</p>
</dd>
<dt><code>group</code></dt><dd><p>a factor with levels <code>hpc</code> and <code>oxalic</code> of the decontaminants used</p>
</dd>
</dl>



<h3>Details</h3>

<p>These data examplify Wadley's problem: counts where the maximum number is not known. The data were analyzed by Trajstman (1989) using a three-parameter logistic model and then re-analyzed by Morgan and Smith (1992) using a three-parameter Weibull type II model. In both cases the authors adjusted for overdispersion (in different ways).
</p>
<p>It seems that Morgan and Smith (1992) fitted separate models for the two decontaminants and using the control group for both model fits. In the example below a joint model is fitted where the control group is used once to determine a shared upper limit at concentration 0.
</p>


<h3>Source</h3>

<p>Trajstman, A. C. (1989) Indices for Comparing Decontaminants when Data Come from Dose-Response Survival and Contamination Experiments, <em>Applied Statistics</em>, <b>38</b>, 481&ndash;494. 
</p>


<h3>References</h3>

<p>Morgan, B. J. T. and Smith, D. M. (1992) A Note on Wadley's Problem with Overdispersion, <em>Applied Statistics</em>, <b>41</b>, 349&ndash;354.
</p>


<h3>Examples</h3>

<pre>

## Wadley's problem using a three-parameter log-logistic model
decon.LL.3.1 &lt;- drm(count~conc, group, data = decontaminants, fct = LL.3(), 
type = "Poisson", pmodels = list(~group, ~1, ~group))

summary(decon.LL.3.1)

plot(decon.LL.3.1)


## Same model fit in another parameterization (no intercepts)
decon.LL.3.2 &lt;- drm(count~conc, group, data = decontaminants, fct=LL.3(), 
type = "Poisson", pmodels = list(~group-1, ~1, ~group-1))

summary(decon.LL.3.2)

</pre>


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